Can We Read Neural Networks? Epistemic Implications of Two Historical Computer Science Papers.
The article discusses two computer science research papers concerning artificial intelligence (AI). Topics explored include the susceptibility of deep convolutional neural networks to input pertubations acknowledged in the 2013 study "Intriguing Properties of Neural Networks," by Christian Szegedy a...
| Published in: | American Literature Vol. 95; no. 2; pp. 423 - 429 |
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| Format: | Article |
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Duke University Press
Jun2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=164087075&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 164087075 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00029831 ALI jtl: American Literature issn: 00029831 maglogo: N pubinfo: dt: Jun2023 vid: 95 iid: 2 pid: 154 pub: Duke University Press artinfo: ui: 164087075 10.1215/00029831-10575218 ppf: 423 ppct: 6 formats: tig: atl: Can We Read Neural Networks? Epistemic Implications of Two Historical Computer Science Papers. aug: au: Offert, Fabian su: Computer science research Artificial intelligence Artificial neural networks Language models Computer programming 21st century (Literary period) sug: subj: Computer science research Artificial intelligence Artificial neural networks Language models Computer programming 21st century (Literary period) ab: The article discusses two computer science research papers concerning artificial intelligence (AI). Topics explored include the susceptibility of deep convolutional neural networks to input pertubations acknowledged in the 2013 study "Intriguing Properties of Neural Networks," by Christian Szegedy and colleagues, and the capability of sequence-to-sequence language models to execute short computer programs reported in the 2014 study "Learning to Execute," by Wojciech Zaremba and Ilya Sutskever. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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